Dataset Information

Availability and inequality in accessibility of health centre-based primary healthcare in Ethiopia.

ABSTRACT: BACKGROUND:Achieving fair access to healthcare and improving population health are crucial in all settings. Properly staffed and fairly distributed primary health care (PHC) facilities are prerequisites to ensure accessible healthcare services. Nevertheless, availability and accessibility issues are common public health concerns, especially in under-resourced countries including Ethiopia. Measuring inequalities in accessibility of healthcare resources guide policy decisions to improve PHC services and ultimately achieving universal health coverage (UHC). PURPOSE:To assess availability and measure magnitude and trend of inequalities in accessibility of health centre-based PHC resources in Ethiopia during 2015 to 2017. METHODS:We conducted a cross-sectional population-based analysis of district-level data collected from 16th December 2017 until 24th May 2018. Afar, Dire-Dawa, and Tigray regions were purposefully included in the study to represent the four pastoralist/semi-pastoralist, three urban and four agrarian regions in Ethiopia, respectively. We used ratios, different inequality indices and Gini decomposition techniques to characterise the inequalities. RESULTS:In 2017, median of health centres (HCs) per 15,000 inhabitants and their Gini indices (GIs) for Afar, Dire-Dawa, and Tigray were 0.781, 0.566, 0.591 vs. 0.237, 0.280, 0.216 respectively. Median overall skilled health workers (SHWs) per 10,000 inhabitants were 5.250, 7.539, and 6.246, respectively. These accounted for 11.80%, 16.94% and 14.04% of the WHO target of 44.5 to achieve SDGs. The corresponding GIs for the regions were 0.347, 0.186 and 0.175. Despite a higher overall SHWs inequality in the urban districts of Tigray (GI = 0.301), only Tigray showed significant inequality reductions in GHE (p < 0.001) and in all categories of SHWs (p < 0.05). CONCLUSIONS:Our analysis provided a clear picture of availability and inequalities in PHC resources across three regions in Ethiopia. Identifying contributing factors to low densities and high inequalities of SHWs may help improve PHC services nationwide, along with pathway towards UHC.

SUBMITTER: Woldemichael A

PROVIDER: S-EPMC6440637 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Availability and inequality in accessibility of health centre-based primary healthcare in Ethiopia.

Woldemichael Abraha A Takian Amirhossein A Akbari Sari Ali A Olyaeemanesh Alireza A

PloS one 20190329 3

<h4>Background</h4>Achieving fair access to healthcare and improving population health are crucial in all settings. Properly staffed and fairly distributed primary health care (PHC) facilities are prerequisites to ensure accessible healthcare services. Nevertheless, availability and accessibility issues are common public health concerns, especially in under-resourced countries including Ethiopia. Measuring inequalities in accessibility of healthcare resources guide policy decisions to improve PH ...[more]

PMID: 30925153

Similar Datasets

Project description:Community-based health insurance (CBHI) as a demand-side intervention is presumed to drive improvements in health services quality, and the quality of health services is an important supple-side factor in motivating CBHI enrollment and retention. There is, however, limited evidence on this interaction. This study examined the interaction between quality of health services and CBHI enrollment and renewal. A mixed-method comparative study was conducted in four agrarian regions of Ethiopia. The study followed the Donabedian model to compare quality of health services in health centers located in woredas/districts that implemented CBHI with those that did not. Data was collected through facility assessments, client-exit interviews, and key informant interviews. In addition to manual thematic analysis of qualitative data, quantitative descriptive and inferential analyses were done using SPSS vs 25. The process related (composite index including provider-client interpersonal communication) and outcome related (client satisfaction) measures of service quality in CBHI woreda/districts differed significantly from non-CBHI woredas/districts, but there were no significant differences in overall measures of structural quality between the two. The study found better diagnostic test capacity, availability of tracer drugs, provider interpersonal communication, and service quality standards in CBHI woredas. A higher proportion of clients at CBHI health centers gave high ratings of overall satisfaction with services. Individual and household factors including family size, age, household health care-related expenditures, and educational status, played a more significant role in CBHI enrollment and renewal decisions than health service quality. Key-informants reported in interviews that participation in the scheme increased accountability of health facilities in CBHI woredas/districts, because they promised to provide quality services using the CBHI premium collected at the beginning of the year from all enrolled households. This study indicates a need for follow-up research to understand the nuanced linkages between quality of care and CBHI enrollment.

Project description:IntroductionHIV testing is the entry point to HIV prevention, care and treatment and needs continuous evaluation to understand whether all social groups have accessed services equally. Addressing disparities in HIV testing between social groups results in effective and efficient response against HIV prevention. Despite these benefits, there was no previous study on inequality and determinants over time in Ethiopia. Thus, the objective of this research was to examine socioeconomic inequality in individuals undertaking HIV testing over time, allowing for the identification of persistent and emerging determinants.MethodsData sources for the current study were the 2011 and 2016 Ethiopian Demographic Health Surveys. The 2016 population health survey is the one that Ethiopia used to set national AIDS response strategies; there was no other recent survey with HIV/AIDS-related indicators in Ethiopia. The final sample size for the current study was 28,478 for the year 2011 and 25,542 for the year 2016. The concentration curve and Erreygers' concentration index were used to estimate socioeconomic inequality in HIV testing. Subsequently, decomposition analysis was performed to identify persistent and emerging contributors of socioeconomic inequality. Generalized linear regression model with the logit link function was employed to estimate the marginal effect, elasticity, Erreygers' concentration index (ECI), and absolute and percentage contributions of each covariate.ResultsThe concentration curve was below the line of equality over time, revealing the pro-rich inequality in HIV testing. The inequality was observed in both 2011 (ECI = 0.200) and 2016 (ECI = 0.213). A household wealth rank had the highest percentage contribution (49.2%) for inequality in HIV testing in 2011, which increased to 61.1% in 2016. Additional markers include listening to the radio (13.4% in 2011 and 12.1% in 2016), education status (8.1% in 2011 and 6.8% in 2016), and resident (-2.0% in 2011 and 6.3% in 2016). Persistent determinants of individuals undertaking HIV testing were age 20-34 years, geographic region, education status, marital status, religion, income, media exposure (listening to the radio, reading newspaper, watching television), knowledge about HIV/AIDS, and attitudes towards people living with HIV. Age between 35 and 44 years and urban residence emerged as new associated factors in 2016.ConclusionsThe higher HIV testing coverage was among individuals with higher socioeconomic status in Ethiopia. Socioeconomic inequality amongst individuals undertaking HIV testing was diverging over time. Household wealth rank, mass media exposure, education status, and resident took the largest share in explaining the disparity in individuals undertaking HIV testing between the lower and higher income groups. Therefore, interventions to equalise HIV testing coverage should take account of these determinants.

Project description:Background: Adopting Universal Health Coverage for implementation of a national health insurance system [Jaminan Kesehatan Nasional (JKN)/Badan Penyelenggara Jaminan Sosial or the Indonesian National Social Health Insurance Scheme (BPJS)] targets the 255 million population of Indonesia. The availability, accessibility, and acceptance of healthcare services are the most important challenges during implementation. Referral behavior and the utilization of primary care structures for underserved (rural/remote regions) populations are key guiding elements. In this study, we provided the first assessment of BPJS implementation and its resulting implications for healthcare delivery based on the entire insurance dataset for the initial period of implementation, specifically focusing on poor and remote populations. Methods: Demographic, economic, and healthcare infrastructure information was obtained from public resources. Data about the JKN membership structure, performance information, and reimbursement were provided by the BPJS national head office. For analysis, an ANOVA was used to compare reimbursement indexes for primary healthcare (PHC) and advanced healthcare (AHC). The usage of primary care resources was analyzed by comparing clustered provinces and utilization indices differentiating poor [Penerima Bantuan Iur (PBI) membership] and non-poor populations (non-PBI). Factorial and canonical discrimination analyses were applied to identify the determinants of PHC structures. Results: Remote regions cover 27.8% of districts/municipalities. The distribution of the poor population and PBI members were highly correlated (r 2 > 0.8; p < 0.001). Three clusters of provinces [remote high-poor (N = 13), remote low-poor (N = 15), non-remote (N = 5)] were identified. A discrimination analysis enabled the >82% correct cluster classification of infrastructure and human resources of health (HRH)-related factors. Standardized HRH (nurses and general practitioners [GP]) availability showed significant differences between clusters (p < 0.01), whereas the availability of hospital beds was weakly correlated. The usage of PHC was ~2-fold of AHC, while non-PBI members utilized AHC 4- to 5-fold more frequently than PBI members. Referral indices (r 2 = 0.94; p < 0.001) for PBI, non-PBI, and AHC utilization rates (r 2 = 0.53; p < 0.001) were highly correlated. Conclusion: Human resources of health availability were intensively related to the extent of the remote population but not the numbers of the poor population. The access points of PHC were mainly used by the poor population and in remote regions, whereas other population groups (non-PBI and non-Remote) preferred direct access to AHC. Guiding referral and the utilization of primary care will be key success factors for the effective and efficient usage of available healthcare infrastructures and the achievement of universal health coverage in Indonesia. The short-term development of JKN was recommended, with a focus on guiding referral behavior, especially in remote regions and for non-PBI members.

Project description:BackgroundWith universal health coverage a key component of the 2030 Sustainable Development Goals, targeted monitoring is crucial for reducing inequalities in the provision of services. However, monitoring largely occurs at the national level, masking sub-national variation. Here, we estimate indicators for measuring the availability and geographical accessibility of services, at national and sub-national levels across sub-Saharan Africa, to show how data at varying spatial scales and input data can considerably impact monitoring outcomes.MethodsAvailability was estimated using the World Health Organization guidelines for monitoring emergency obstetric care, defined as the number of hospitals per 500,000 population. Geographical accessibility was estimated using the Lancet Commission on Global Surgery, defined as the proportion of pregnancies within 2 h of the nearest hospital. These were calculated using geo-located hospital data for sub-Saharan Africa, with their associated travel times, along with small area estimates of population and pregnancies. The results of the availability analysis were then compared to the results of the accessibility analysis, to highlight differences between the availability and geographical accessibility of services.ResultsDespite most countries meeting the targets at the national level, we identified substantial sub-national variation, with 58% of the countries having at least one administrative unit not meeting the availability target at province level and 95% at district level. Similarly, 56% of the countries were found to have at least one province not meeting the accessibility target, increasing to 74% at the district level. When comparing both availability and accessibility within countries, most countries were found to meet both targets; however sub-nationally, many countries fail to meet one or the other.ConclusionWhile many of the countries met the targets at the national level, we found large within-country variation. Monitoring under the current guidelines, using national averages, can mask these areas of need, with potential consequences for vulnerable women and children. It is imperative therefore that indicators for monitoring the availability and geographical accessibility of health care reflect this need, if targets for universal health coverage are to be met by 2030.

Dataset Information

Availability and inequality in accessibility of health centre-based primary healthcare in Ethiopia.

Publications

Availability and inequality in accessibility of health centre-based primary healthcare in Ethiopia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets